Systems and methods for assessing risk of potential decisions

ABSTRACT

A method implemented by a processor of a device, the method including determining one or more user values, determining a prompt requiring a decision, and receiving a user selection to one or more choice options related to the one or more user values and pertaining to the prompt requiring a decision. The method further includes comparing the user selection to a risk-neutral utility function and assessing a risk of a potential decision based on the comparison.

TECHNICAL FIELD

The present specification generally relates to risk assessment systems,in particular, to risk assessment systems that assess the risk of apotential decision based on comparison between user responses to choiceoptions and user values.

BACKGROUND

It is estimated that adults make 35,000 choices, on average, per day.Individuals generally attempt to make the best decisions possible, basedon their own unique set of values and priorities with the limited timeand information available to them. However, people are prone to makingirrational or risky decisions that do not, in fact, align with theirpersonal values and priorities. These risky decisions are ofteninfluenced by psychological, cognitive biases and limited time andeconomic resources. It is difficult for individuals to identify when adecision is deviating from their supposed values in real-time. Moreover,even if an individual is able to realize the decision she is consideringdoes not align with her personal values, it is equally difficult for theindividual to identify why she is making a potentially risky decision,or a decision that deviates from her expected behavior.

Accordingly, a need exists for systems that assess the risk of apotential decision to a prompt requiring a decision and inform a user ofthe risk of the potential decision.

SUMMARY

In one embodiment, a method implemented by a processor of a device,includes determining one or more user values, determining a promptrequiring a decision, and receiving a user selection to one or morechoice options related to the one or more user values and pertaining tothe prompt requiring a decision. The method further includes comparingthe user selection to a risk-neutral utility function and assessing arisk of a potential decision based on the comparison.

In another embodiment, a system includes a device including a processorconfigured to determine one or more user values, determine a promptrequiring a decision, and receive a user selection to one or more choiceoptions related to the one or more user values and pertaining to theprompt requiring a decision. The processor is further configured tocompare the user selection to a risk-neutral utility function and assessa risk of a potential decision based on the comparison.

In yet another embodiment, a processor of a computing device isconfigured to determine one or more user values, determine a promptrequiring a decision, and receive a user selection to one or more choiceoptions related to the one or more user values and pertaining to theprompt requiring a decision. The processor is further configured tocompare the user selection to a risk-neutral utility function and assessa risk of a potential decision based on the comparison.

These and additional features provided by the embodiments describedherein will be more fully understood in view of the following detaileddescription, in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments set forth in the drawings are illustrative and exemplaryin nature and not intended to limit the subject matter defined by theclaims. The following detailed description of the illustrativeembodiments can be understood when read in conjunction with thefollowing drawings, where like structure is indicated with likereference numerals and in which:

FIG. 1 schematically depicts an example operating environment of thesystem for assessing a risk of a potential decision of the presentdisclosure, according to one or more embodiments shown and describedherein;

FIG. 2 schematically depicts non-limiting components of the devices ofthe system for assessing a risk of a potential decision of the presentdisclosure, according to one or more embodiments shown and describedherein;

FIG. 3 depicts a flowchart for a method of assessing a risk of apotential decision, according to one or more embodiments shown anddescribed herein; and

FIG. 4 schematically depicts an example user profile screen on a userdevice of the system of FIG. 2, according to one or more embodimentsshown and described herein.

FIG. 5 schematically depicts an example interaction between a user andthe system of FIG. 2 on a user device of the system of FIG. 2, accordingto one or more embodiments shown and described herein.

FIG. 6 schematically depicts an example choice option presented on auser device of the system of FIG. 2, according to one or moreembodiments shown and described herein.

DETAILED DESCRIPTION

Embodiments described herein are directed to systems and methods fordetermining a risk of a potential decision. The system collects data ona user to determine one or more values of the user that influence theuser's decision-making process. The system also collects data todetermine a weight of each of the values, indicating an extent to whichthe user considers each value when making a decision. Based on theweighted values, the system generates a risk-neutral utility function,indicating a perfectly neutral decision-making process thatappropriately considers each value of the user. The system compares apotential decision to the risk-neutral utility function to determine therisk of the potential decision. More particularly, the system determinesa prompt, or ultimate question the user desires guidance on, requiring adecision. The system then presents the user with one or more choiceoptions pertaining to the prompt requiring a decision and related to theuser values. That is, the choice options provide information on theprompt requiring a decision that relate to the values of the user, andthe answers or selections to the choice options provide clarity to theuser and the system as to what potential decision the user is likely tomake. The system may generate an expected utility function based on theanswers to the choice options, indicating what values were, in fact,considered by the user, and to what extent, when answering the choiceoptions. By comparing the risk-neutral utility function and the expectedutility function, the system may then determine the risk of thepotential decision, or the extent that the potential decision deviatesfrom a risk-neutral, anticipated decision or decision-making process ofthe user. Various embodiments of the risk assessment system and theoperation of the system are described in more detail herein. Wheneverpossible, the same reference numerals will be used throughout thedrawings to refer to the same or like parts.

Referring now to the drawings, FIG. 1 schematically depicts an exampleoperating environment of a system 100 for assessing a risk of apotential decision of the present disclosure, according to one or moreembodiments shown and described herein. As illustrated, FIG. 1 depicts auser 102 operating a user device 103. The user device 103 may be apersonal electronic device of the user 102. The user device 103 may beused to perform one or more user-facing functions, such as receiving oneor more inputs from the user 102 or providing information to the user102. The user device 103 may be a cellular phone, tablet, or personalcomputer of the user 102. The user device 103 includes a processor forassessing the risk of a potential decision to a prompt requiring adecision. Merely as an example, the prompt requiring a decision may bewhether the user should purchase vehicle 110 or vehicle 112, and thepotential decision may be to purchase one of the vehicles 110 or 112over the other.

Referring now to FIG. 2, non-limiting components of the user device 103of the system 100 for assessing a risk of a potential decision of thepresent disclosure are schematically depicted, according to one or moreembodiments shown and described herein. The user device 103 includes acontroller 200 including a processor 202, a memory module 204, and adata storage component 206. The user device 103 may further include aninterface module 146, a network interface hardware 150, and acommunication path 208. It should be understood that the user device 103of FIG. 2 is provided for illustrative purposes only, and that otheruser devices 103 comprising more, fewer, or different components may beutilized.

Referring now to FIGS. 1 and 2, the processor 202 may be any devicecapable of executing machine readable and executable instructions.Accordingly, the processor 202 may be a controller, an integratedcircuit, a microchip, a computer, or any other computing device. Thecontroller 200, including the processor 202, is coupled to thecommunication path 208 that provides signal interconnectivity betweenvarious modules of the user device 103. Accordingly, the communicationpath 208 may communicatively couple any number of processors 202 withinthe user device 103 with one another, and allow the modules coupled tothe communication path 208 to operate in a distributed computingenvironment. Specifically, each of the modules may operate as a nodethat may send and/or receive data. As used herein, the term“communicatively coupled” means that coupled components are capable ofexchanging data signals with one another such as, for example,electrical signals via conductive medium, electromagnetic signals viaair, optical signals via optical waveguide s, and the like.

Accordingly, the communication path 208 may be formed from any mediumthat is capable of transmitting a signal such as, for example,conductive wires, conductive traces, optical waveguides, or the like. Insome embodiments, the communication path 208 may facilitate thetransmission of wireless signals, such as WiFi, Bluetooth®, Near FieldCommunication (NFC) and the like. Moreover, the communication path 208may be formed from a combination of mediums capable of transmittingsignals. In one embodiment, the communication path 208 comprises acombination of conductive traces, conductive wires, connectors, andbuses that cooperate to permit the transmission of electrical datasignals to components such as processors, memories, sensors, inputdevices, output devices, and communication devices. Additionally, it isnoted that the term “signal” means a waveform (e.g., electrical,optical, magnetic, mechanical or electromagnetic), such as DC, AC,sinusoidal-wave, triangular-wave, square-wave, vibration, and the like,capable of traveling through a medium.

The controller 200 of the user device 103 includes the memory module204. The controller 200, including the memory module 204, is coupled tothe communication path 208. The memory module 204 may comprise RAM, ROM,flash memories, hard drives, or any device capable of storing machinereadable and executable instructions such that the machine readable andexecutable instructions can be accessed by the processor 202. Themachine readable and executable instructions may comprise logic oralgorithm(s) written in any programming language of any generation(e.g., 1GL, 2GL, 3GL, 4GL, or 5GL) such as, for example, machinelanguage that may be directly executed by the processor, or assemblylanguage, object-oriented programming (OOP), scripting languages,microcode, etc., that may be compiled or assembled into machine readableand executable instructions and stored on the memory module 204.Alternatively, the machine readable and executable instructions may bewritten in a hardware description language (HDL), such as logicimplemented via either a field-programmable gate array (FPGA)configuration or an application-specific integrated circuit (ASIC), ortheir equivalents. Accordingly, the methods described herein may beimplemented in any conventional computer programming language, aspre-programmed hardware elements, or as a combination of hardware andsoftware components.

Still referring to FIG. 2, the user device 103 comprises networkinterface hardware 150 for communicatively coupling the user device 103to the external device 130. The network interface hardware 150 can becommunicatively coupled to the communication path 208 and can be anydevice capable of transmitting and/or receiving data via a network.Accordingly, the network interface hardware 150 can include acommunication transceiver for sending and/or receiving any wired orwireless communication. For example, the network interface hardware 150may include an antenna, a modem, LAN port, Wi-Fi card, WiMax card,mobile communications hardware, near-field communication hardware,satellite communication hardware and/or any wired or wireless hardwarefor communicating with other networks and/or devices. In one embodiment,the network interface hardware 150 includes hardware configured tooperate in accordance with the Bluetooth® wireless communicationprotocol. The network interface hardware 150 of the user device 103 maytransmit information on a prompt requiring a decision to the externaldevice 130. The network interface hardware 150 may also receiveinformation and data relating to the prompt requiring a decision fromthe external device 130.

In some embodiments, the user device 103 may be communicatively coupledto the external device 130 by the network 120. In one embodiment, thenetwork 120 may include one or more computer networks (e.g., a personalarea network, a local area network, or a wide area network), cellularnetworks, satellite networks and/or a global positioning system andcombinations thereof. Accordingly, the user device 103 can becommunicatively coupled to the network 120 via a wide area network, viaa local area network, via a personal area network, via a cellularnetwork, via a satellite network, etc. Suitable local area networks mayinclude wired Ethernet and/or wireless technologies such as, forexample, wireless fidelity (Wi-Fi). Suitable personal area networks mayinclude wireless technologies such as, for example, IrDA, Bluetooth®,Wireless USB, Z-Wave, ZigBee, and/or other near field communicationprotocols. Suitable cellular networks include, but are not limited to,technologies such as LIE, WiMAX, UMTS, CDMA, and GSM.

The external device 130 may be any database server or electronic devicebelonging to the user 102 or a third party. For instance, the externaldevice 130 may contain one or more storage devices for storing datapertaining to the operation of the system 100 for assessing a risk of apotential decision. The external device 130 may function as a generaldatabase for transmitting data relating to a prompt requiring adecision, as discussed in further detail below.

The user device 103 comprises the interface module 146. The interfacemodule 146 may be coupled to the communication path 208. The interfacemodule 146 includes one or more user/machine interfaces to allowpresentation of data or information to the user 102 and/or allow forinput of user information to the user device 103. For instance, theinterface module 146 may include a visual interface 144. The visualinterface 144 may be, for example, a cathode ray tube, light emittingdiodes, a liquid crystal display, a plasma display, or the like.Moreover, the visual interface 144 may be a touchscreen that, inaddition to providing an optical display, detects the presence andlocation of a tactile input upon a surface of or adjacent to the visualinterface 144. The interface module 146 may also include audialinterface 142. The audial interface 142 may include one or more speakersto output an audio message to the user 102. The audial interface 142 mayalso include a microphone to receive audio input, such as vocalcommands, from the user 102.

Referring again to the memory module 204 of the controller 200 of theuser device 103, the programming instructions stored in the memorymodule 204 may be embodied as a plurality of software logic modules,where each logic module provides programming instructions for completingone or more tasks. Each of the logic modules may be embodied as acomputer program, firmware, or hardware, as an example. Illustrativeexamples of logic modules present in the memory module 204 include, butare not limited to, value logic 210, prompt logic 212, data receivinglogic 214, choice option logic 216, communication logic 218, utilityfunction logic 220, risk logic 222, and neural network training logic224.

The value logic 210 includes one or more programming instructions fordetermining, and assigning weights to, values of the user 102. As usedherein, the term “value” generally refers to the personal principlesheld by the user 102. The values of the user 102 may also be thought ofas the core beliefs and convictions that the user 102 bases her life anddecisions on. Merely as examples, user values may include personalhealth and fitness, economic frugality, environmental friendliness, apreference for local businesses, loyalty, higher learning, privacy, andcharitableness. It should be appreciated that any number and combinationof values, beyond those listed, may be important to the user 102.

The value logic 210 includes programming instructions for receivinginput from the user 102 providing a list of values important to the user102. The value logic 210 also includes programming instructions forpredicting the values of the user 102. For instance, the value logic 210may include programming to predict the values of the user 102 based onuser-specific data and/or community data. For instance, the controller200 may receive information concerning the age, gender, address ofresidence, education, and/or profession of the user 102 and predict thevalues of the user 102 based on the information. The user-specificinformation used to predict the values of the user 102 may be stored inthe data storage component 206 and derived from one or more applicationsassociated with the user device 103. For instance, the values of theuser 102 may be predicted based on information derived from a personalemail application of the user device 103, a personal calendarapplication of the user device 103, a personal banking application ofthe user device 103, and the like.

The value logic 210 may also include programming to leverage communitydata to predict the values of the user 102. For example, through theexternal device 130 and network interface hardware 150, the controller200 may receive information on the values of other users in the samedemographic, geographic community, or in the personal contacts of theuser device 103 of the user 102 and use this information to predict thevalues of the user 102. The value logic 210 may also include programminginstructions to assign a weight to each of the values of the user 102.The value logic 210 therefore includes programming for receiving inputfrom the user 102 providing a weight to each of the values of the user102. The weight may be a numerical value associated with the importanceof the personal value of the user 102. For instance, a first personalvalue with a weight of 10 may be considered more important to the user102 than a second personal value with a weight of 2. The value logic 210also includes programming to predict the weights of the values of theuser 102 based on user-specific data and/or community data.

The prompt logic 212 includes one or more programming instructions fordetermining a prompt requiring a decision. The prompt logic 212 includesprogramming instructions for receiving input from the user 102 providinga prompt requiring a decision. The prompt requiring a decision may be aquestion requiring an answer. For instance, the prompt may be a singlequestion that the user 102 desires a yes or no answer to, such as,“Should I buy vehicle X?” (e.g. the vehicle 110). The prompt may also bea question with multiple, but limited, possible answers, such as,“Should I buy car X, Y, or Z?” (e.g. the vehicles 110 and 112). Theprompt may also be a general informational prompt, such as, “I'mthinking about buying a car.”

The user 102 may provide the prompt requiring a decision through theinterface module 146. The prompt logic 212 may also include programmingfor generating a prompt requiring a decision based on user-specific dataor information. The user-specific information may be stored in the datastorage component 206 and derived from one or more applicationsassociated with the user device 103, as discussed above. For instance,the prompt logic 212 may include programming to determine, based oncorrespondence in a personal email application of the user device 103 oran electronic receipt saved on the user device 103, that a vehicle leaseof the user 102 is expiring in the near future.

The prompt logic 212 may also include programming to generate a promptsuch as, “Do you want help deciding to buy or lease a vehicle?” Theprompt may be presented to the user 102 along with a notification,allowing the user 102 to ignore the prompt or begin a risk assessmentexercise regarding the generated prompt. While the prompts discussed asexamples have been particularly related to purchasing or leasing avehicle, it should be understood that the prompt may relate to anynumber of decisions that the user 102 must make or desires assistancemaking. For instance, the prompt requiring a decision may relate topurchasing a house, receiving a vaccine or other medical procedure, whatto eat for dinner, whether to take a vacation, and the like.

The data receiving logic 214 includes one or more programminginstructions for receiving data from the external device 130. That is,the data receiving logic 214 includes programming to cause a connectionbetween the network interface hardware 150 and the external device 130such that data transmitted by the external device 130 is received by thecontroller 200. Further, the data transmitted by the external device 130may be stored (e.g., within the data storage component 206). The datatransmitted by the external device 130 may relate to the promptrequiring a decision. For instance, the network interface hardware 150may communicate a prompt requiring a decision that is determined withthe prompt logic 212 to the external device 130, soliciting data fromthe external device 130 relating to the prompt requiring a decision. Forexample, if the prompt requiring a decision relates to whether the usershould purchase vehicle 110 or vehicle 112, where the vehicles 110, 112are different makes and/or models, for instance, the external device 130may transmit data to the controller 200 including statistics, data, andinformation on vehicle 110 and vehicle 112.

The choice option logic 216 includes one or more programminginstructions for generating choice options for the user 102. The choiceoptions are questions or tasks pertaining to the prompt requiring adecision. The choice options also relate to the values of the user 102.For instance, the values of the user 102 may be economic frugality,preference for locally sourced products, and environmental friendliness,and the prompt requiring a decision may relate to purchasing a vehicle.In such a case, the one or more choice options will pertain to theprompt requiring a decision in that the one or more choice options willallow the user 102 to indicate whether the user 102 is interested inpurchasing the vehicle in question or not. The choice options generatedwill relate to the values of the user 102 in that the choice optionswill present information to the user 102 on the cost, environmentalimpact, and location of manufacture of one or more vehicles beingconsidered to purchase.

In a first example, the choice options may present the cost,environmental impact, and location of manufacture of the vehicle 110 andask the user 102 if she wishes to purchase the vehicle 110 with thatknowledge. As another example, the choice options may present the cost,environmental impact, and location of manufacture of the vehicle 110 andthe vehicle 112 and ask the user 102 if she wishes to purchase thevehicle 110 or the vehicle 112 with that knowledge. As yet anotherexample, the one or more choice options may present the cost,environmental impact, and location of manufacture of the vehicle 110,the vehicle 112, and a third vehicle X (not depicted) and ask the user102 to rank the vehicles in order of preference with that knowledge.

Each choice option may relate to a single value of the user 102. Forinstance, a first choice option may ask the user 102 to state apreference between the vehicle 110 and the vehicle 112 based on theemissions, miles per gallon, and other environmental statistics relatingto the vehicles. A second choice option may ask the user 102 to state apreference between the vehicle 110 and the vehicle 112 based on thepurchase price of the vehicles, average cost of repair, and othereconomic statistics relating to the vehicles. In other cases, eachchoice option may relate to a combination of values of the user 102. Forinstance, a single choice option may be generated that presents all dataon the vehicle 110 and the vehicle 112 relating to the values of theuser 102. As an example, the cost, environmental impact, and location ofmanufacture of the vehicle 110 and the vehicle 112 may be presented tothe user 102 at once and the user 102 may be prompted to state apreference between the two vehicles. The data and information of thevehicles 110, 112 presented in the choice options may be provided by theexternal device 130 based on the prompt requiring a decision, asdiscussed above. The choice option logic 216 may include programming tofilter the data provided by the external device 130 on the vehicle 110and the vehicle 112 and present only information related to the valuesof the user 102 in the generated choice options.

The communication logic 218 includes one or more programminginstructions for communicating with the user 102 through the interfacemodule 146. For instance, the communication logic 218 may includeprogramming that allows information to be provided to, and receivedfrom, the user 102 in the form of a chat bot. For example, choiceoptions may be presented on the visual interface 144 in the form oftext, and the user 102 may type responses or otherwise provideselections (e.g. answering a multiple choice question) to the choiceoptions. The communication logic 218 also includes programming thatallows information to be provided to, and received from, the user 102audibly. For instance, choice options may be presented to the user 102as an audial message through the audial interface 142, and the user 102may respond to the choice options through voice commands and responsesthrough the audial interface 142.

The user 102 may interact with the system visually and audiblysimultaneously. For instance, choice options may be presented to theuser 102 as text, and the user 102 can respond to the choice optionsthrough voice command. The communication logic 218 may also includeprogramming that allows the user 102 to provide information to thecontroller 200 through a camera of the user device 103. For instance,the user 102 may take a photo or video of a particular vehicle, and theprompt logic 212 may include programming to generate a prompt requiringa decision, such as, “Are you interested in purchasing the vehicle inthe photo?” based on the photo. If the user 102 answers in theaffirmative, the system may begin a risk assessment exercise regardingthe purchasing of the photographed vehicle.

The utility function logic 220 includes one or more programminginstructions for generating utility functions. The utility functionlogic 220 includes programming that allows for the generation of arisk-neutral utility function based on the values of the user 102. Therisk-neutral utility function may be a linear combination of theweighted values of the user 102. The risk-neutral utility functionrepresents a decision of the user 102 having no risk. For instance, therisk-neutral utility function effectively indicates that when making adecision, the user 102 should consider a first value of the user 102,such as economic frugality to a first extent indicated by the weight ofthe value, and the user 102 should consider a second value, such asenvironmental friendliness, to a second extent indicated by the weightof the value.

The utility function logic 220 also includes programming that allows forthe generation of an expected utility function based on the responses tothe choice options provided by the user 102. For instance, the utilityfunction logic 220 includes programming to fit a learned model to theresponses to the choice options provided by the user 102 to generate theexpected utility function. The expected utility function represents acombination of weighted values of the user 102 based on the responses tothe choice options. In other words, the expected utility functioneffectively represents what values the user 102 is actually consideringin answering the choice options, and to what extent the user 102 isweighing or considering each of the values.

The risk logic 222 includes one or more programming instructions forassessing a risk of a potential decision of the user 102. For instance,the risk logic 222 may include programming to determine a distancebetween the risk-neutral utility function of the user 102 based on thevalues of the user 102 and the expected utility function of the user 102based on the responses to the choice options. More particularly, therisk logic 222 may include instructions to plot a curve generated fromthe risk-neutral utility function and a curve generated from theexpected utility function in space, and determine a distance between thetwo curves. The greater the distance between the risk-neutral utilityfunction and the expected utility function, the greater the risk of thepotential decision indicated by the answers provided by the user 102 tothe choice options.

Risk, as described herein, may be considered as the magnitude of thedistance between the risk-neutral utility function and the expectedutility function. For instance, the user 102 may be a naturallyrisk-seeking person, or a person who values financially aggressivedecisions, for example. If the responses provided to the choice optionsindicate that the user 102 is approaching the prompt requiring adecision with a conservative financial approach, this discrepancy maystill be considered a “risk” herein, as it deviates from the anticipatedor traditional decision-making process and values of the user 102. Therisk logic 222 may also include programming to determine whether apotential decision to the prompt requiring a decision based on theresponses to the choice options provided by the user 102 is risk-seekingor risk-averse. That is, based on the comparison between therisk-neutral utility function and the expected utility function, thepotential decision provided by the user 102 may be determined to be moreor less risky (risk-seeking or risk-averse) than the expected behaviorof the user 102 based on her values.

Whether a potential decision is a risk, either risk-averse orrisk-seeking, may be determined based on the magnitude of the distancebetween the risk-neutral utility function and the expected utilityfunction. That is, if the magnitude of the distance between therisk-neutral utility function and the expected utility function exceedsa pre-determined threshold, the potential decision may be a risk. Incontrast, if the magnitude of the distance between the risk-neutralutility function and the expected utility function does not exceed thepre-determined threshold, the potential decision of the user 102 may notbe a risk. Merely as an example, if the responses to the choice optionsprovided by the user 102 exactly align with the values of the user 102,there may be no distance between the risk-neutral utility function andthe expected utility function, indicating that the potential decisionexactly aligns with the values of the user 102 and there is noassociated risk with the potential decision.

The risk logic 222 may also include programming for identifying adecision to the prompt requiring a decision that minimizes risk for theuser 102. That is, the risk logic 222 may include programming forgenerating an expected utility function associated with each possibledecision to the prompt requiring a decision. For instance, if the promptrequiring a decision is whether the user 102 should purchase vehicle110, vehicle 112, or a third vehicle (not depicted), an expected utilityfunction may be generated for the decision to purchase each of thevehicles. The distance between each expected utility function and therisk-neutral utility function may be measured, and the expected utilityfunction closest to the risk-neutral utility function may be identified.Therefore, the risk logic 222 may include programming to inform the user102 of the decision to purchase the vehicle that minimizes risk based onthe values of the user 102.

The neural network training logic 224 includes one or more programminginstructions for utilizing a neural network or other machine learningmodel to adjust or improve the operation of one or more other logicmodules of the memory module 204. For instance, the neural networktraining logic 224 may include programming to train the utility functionlogic 220 to improve the accuracy of the modeling used to determine theexpected utility function. The neural network training logic 224 mayalso include programming for training the value logic 210. For instance,the values of the user 102 may change over time. The values the user 102deems important as a student may not be the same values the user 102deems important as an employed graduate. The values of the user 102 mayalso gradually shift as the user 102 ages.

The values of the user 102, and the weights of the values, may bere-determined over time, for instance. As an example, economic frugalitymay initially be considered a core value of the user 102 with a largeweight associated with it. However, if the user 102 continuously shows apreference for spending large sums of money or making risky financialinvestments based on the responses to choice options generated fordifferent prompts requiring a decision, it may be determined that theuser 102 is not as frugal as initially determined. Accordingly, theeconomic values and weights of the values of the user 102 may be learnedand adjusted over time.

Still referring to FIGS. 1 and 2, data storage component 206 maygenerally be a storage medium. Data storage component 206 may containone or more data repositories for storing data that is received and/orgenerated. The data storage component 206 may be any physical storagemedium, including, but not limited to, a hard disk drive (HDD), memory,removable storage, and/or the like. While the data storage component 206is depicted as a local device, it should be understood that the datastorage component 206 may be a remote storage device, such as, forexample, a server computing device, cloud based storage device, or thelike. Illustrative data that may be contained within the data storagecomponent 206 includes, but is not limited to, value data, value weightdata, prompt data, choice option data, utility function data, riskassessment data, and training data.

The value data may generally be data that is used by the controller 200to determine values of the user 102. The value weight data may generallybe data that is used by the controller 200 to determine weights of thevalues of the user 102. The prompt data may generally be data that isused by the controller 200 to determine a prompt requiring a decisionfrom the user 102. The choice option data may generally be data that isused by the controller 200 to determine choice options pertaining to theprompt requiring a decision and related to the values of the user 102.The utility function data may generally be data that is used by thecontroller 200 to determine the risk-neutral utility function based onthe values of the user 102 and the expected utility function based onthe user 102 responses to the choice options. The risk assessment datamay generally be data that is used by the controller 200 to assess therisk of a potential decision to the prompt requiring a decision. Thetraining data may generally be data that is generated as a result of oneor more machine learning processes used to improve the accuracy of therisk-neutral utility function and expected utility function, forinstance.

FIG. 3 depicts flowchart for a method 300 of assessing the risk of apotential decision. The method 300 may be executed based on instructionsstored in the memory module 204 that are executed by the processor 202.FIGS. 4-6 schematically depict example user 102 interactions with theuser device 103 through the interface module 146 (FIG. 2) according tothe method 300 of operation of the system 100.

Referring now to FIGS. 1-4, at block 302 of the method 300, the system100 determines one or more values of the user 102. At block 302 thesystem 100 may receive input from the user 102 indicating the values ofthe user 102. In some examples, instead of or in addition to, receivinginput from the user 102, the system 100 may predict one or values of theuser 102 based on user-specific or community data. In some embodiments,when using the risk assessment system 100 for the first time, the user102 may create a user profile to initially provide the system 100 withvalues of the user 102. The user 102 may also access the user profile atany point in the future to adjust one or more values of the user 102.

With particular reference to FIG. 4, an example user profile screen isdepicted on the user device 103. The user 102 may select a value from adrop-down menu listing a plurality of potential values. In otherembodiments, the user 102 may type a value into a text box. While FIG. 4depicts the user 102 directly providing the system 100 with one or morevalues of the user 102, as noted above, the user 102 may also instructthe system 100 to predict the values of the user 102 through an input onthe user device 103.

Referring again to FIGS. 1-4, at block 304 of the method 300, the system100 determines a weight of each user value to generate one or moreweighted user values. At block 304 the system 100 may receive input fromthe user 102 indicating the weights of the values of the user 102. Insome examples, instead of, or in addition to, receiving input from theuser 102, the system 100 may predict the weights of one or more valuesof the user 102 based on user-specific or community data. In someembodiments, when using the risk assessment system 100 for the firsttime, the user 102 may create a user profile to initially provide thesystem 100 with weights of values of the user 102. The user 102 may alsoaccess the user profile at any point in the future to adjust the weightsof the one or more values of the user 102.

With particular reference to FIG. 4, an example user profile screen isdepicted on the user device 103. The user 102 may select a weight from adrop-down menu listing a plurality of potential weights. The weights maybe numeric values, descriptive words (e.g. low, medium, high), shades ofcolors (e.g. a darker shade indicating a greater weight of the value),and the like. In other embodiments, the user 102 may type a weight intoa text box. As shown in FIG. 4, the user 102 may assign a weight of “3”to an “environmental friendliness” value. On the exemplary 1-5 scaledepicted in FIG. 4, a weight of “3” may indicate that environmentalfriendliness is moderately important to the user 102. A weight of “5”may indicate a high importance, and a weight of “1” may indicate a lowimportance, for instance. It should be appreciated that such a numericscale is merely an example, however, and that the ranges of the scalemay take any desirable value.

While FIG. 4 depicts the user 102 directly providing the system 100 witha weight to a value of the user 102, as noted above, the user 102 mayalso instruct the system 100 to predict the weights of the values of theuser 102 through an input on the user device 103. For instance, the usermay instruct the system 100 to predict the weights of the user valuesthrough personal data and/or community data, as indicated by the“determine weights from personal data” and “determine weights fromothers” options depicted in FIG. 4. In some embodiments, when generatinga user profile, the user 102 may simply instruct the system 100 toassign equal weights to the values of the user 102. For instance, theuser 102 may select cost savings, environmental friendliness, andlocally sourced/manufactured products as values of the user 102. Byinstructing the system 100 to assign equal weights to the values, thesystem may assign a moderate importance, or score of “3,” to each of theselected values. As the user 102 uses the system 100, the system maylearn and tailor the weights of the values of the user 102 based on theanswers provided to choice options and prompts requiring a decision, asmentioned above and discussed in additional detail below.

Referring again to FIGS. 1-4, at block 306 of the method 300, the system100 generates a risk-neutral utility function based on the one or moreweighted user values. The risk-neutral utility function may be a linearcombination of the weighted values of the user 102. The risk-neutralutility function represents a decision of the user 102 having no risk.Merely as an example, if the user 102 assigns a weight of “5” to costsavings, a weight of “3” to environmental friendliness, and a weight of“1” to locally sourced/manufactured products, the risk-neutral utilityfunction would effectively indicate that when making a decision, theuser 102 should highly consider (or be influenced by) cost savings,moderately consider environmental friendliness, and slightly considerlocally sourced/manufactured products.

Referring now to FIGS. 1-5, at block 308 of the method 300, the system100 determines a prompt requiring a decision. At block 308 the system100 may receive input from the user 102 indicating the prompt requiringa decision. For instance, the user 102 may type a prompt requiring adecision, input a prompt requiring a decision through voice commands, orprovide the system 100 with a prompt through a still image or videotaken with a camera of the user device 103, for instance. In someexamples, instead of, or in addition to, receiving input from the user102, the system 100 may predict a prompt requiring a decision. Forinstance, the system 100 may determine a prompt requiring a decisionbased on a search history of the user 102 on the user device 103,electronic receipts stored on the user device 103, emails or othercommunications on the user device 103, and the like. Based on suchinformation the system 100 may determine that the user 102 isconsidering buying a vehicle, buying a house, taking a vacation, and thelike. The system 100 may present an initiating prompt or user inquiryregarding the predicted prompt requiring a decision, asking the user 102if she would like to initiate a risk assessment exercise regarding thepredicted prompt requiring a decision.

With particular reference to FIG. 5, an example interaction between theuser 102 and the system 100 through the interface module 146 of the userdevice 103 is depicted. Text communications generated by the system 100appear on the left of the user device 103, and text communicationsprovided by the user 102 appear on the right of the user device 103. Bythe user 102 indicating that she is interested in buying a car, and isconsidering a first vehicle (make A, model B) and a second vehicle (makeX, model Y), the system 100 may determine that the prompt requiring adecision is whether to buy the first vehicle or the second vehicle. Insome cases, the user 102 may not know which specific vehicles she isinterested in purchasing. For instance, the user 102 may respond “no” tothe system inquiry, “Do you know what car or cars you are considering topurchase?” In such cases, the system 100 may provide the user 102 withfurther questions to determine a prompt requiring a decision. Forinstance, the system 100 may ask, “Do you know if you want to buy atruck, sedan, or sports vehicle?” If the user 102 answers “no,” thesystem 100 may generate choice options relating these and other genericvehicle types.

Based on the risk assessment of the user answers to the choice optionsand the generic prompt requiring a decision, the user 102 may continuethe risk assessment exercise with a more specific prompt requiring adecision. For instance, the user 102 may determine she wishes to buy asports car, and the system 100 may then ask if the user 102 knows whichmake or year of sports car she wants to buy. Such an iterative processmay continue until the user 102 determines a specific vehicle topurchase or no longer wishes to continue with the risk assessmentexercise.

Referring now to FIGS. 1-6, at block 310 of the method 300, the system100 generates one or more choice options related to the one or more uservalues and pertaining to the prompt requiring a decision. Referringparticularly to FIG. 6, a choice option is presented for purchasing thefirst vehicle (make A, model B) and the second vehicle (make X, model Y)in response to the prompt requiring a decision determined at block 308.At block 302, the system 100 may have determined that the values of theuser 102 are cost savings, environmental friendliness, and locallysources/manufactured products. Therefore, the choice option presentedprovides statistics, data, and information on the first and secondvehicle related to these values. For instance, and as shown in FIG. 6,the choice option displays the price, miles per gallon, andmanufacturing source of the first and second vehicles.

It should be appreciated that the choice option may present multipledata points related to each value. For instance, the system 100 maypresent the purchase price and the average cost of repair of thevehicles as information related to cost savings. In the example depictedin FIG. 6, a single choice option is presented including information onthe vehicles related to all of the values of the user 102. However, thesystem 100 may also present a choice option related to each individualvalue of the user 102. For instance, the system may first presentinformation on the vehicles related to cost savings in a first choiceoption and ask the user 102 to indicate a preference between thevehicles based on the first choice option. The system may furtherpresent information on the vehicles related to environmentalfriendliness in a second choice option and ask the user 102 to indicatea preference between the vehicles based on the second choice option.

Referring again to FIGS. 1-6, at block 312 of the method 300, the system100 receives a user selection to the one or more choice options. Theuser selections are provided as answers to the choice options indicatinga preference between the first and second vehicle, for instance. Incases where a single choice option is presented related to all of thedetermined values of the user 102, as depicted in FIG. 6, the user 102may answer the choice option by indicating a vehicle preference, and theanswer to the choice option is therefore also the answer to the promptrequiring a decision.

In cases where multiple choice options are presented, each related to asingle value of the user 102, the system 100 may determine that theultimate decision to the prompt requiring a decision is the decisionthat receives the most “votes” or answers to choice options. That is,the system 100 may present a first choice option related to the costsavings of the first and second vehicles, a second choice option relatedto the environmental friendliness of the first and second vehicles, anda third choice option related to the source of manufacture of the firstand second vehicles. If the user 102 answers that she prefers topurchase the first vehicle in response to the first and second choiceoptions and answers that she prefers to purchase the second vehicle inresponse to the third choice option, the system may determine that theultimate decision to the prompt requiring a decision is a preference topurchase the first vehicle, as the first vehicle received two “votes” incomparison to one “vote” for the second vehicle based on the answers tothe choice options. Referring specifically to the example depicted inFIG. 6, the user 102 responds to the choice option by indicating apreference to purchase the second vehicle (make X, model Y) over thefirst vehicle (make A, model B).

Referring again to FIGS. 1-6, at block 314 of the method 300, the system100 generates an expected utility function based on the user selectionsto the one or more choice options. The expected utility functionrepresents a combination of weighted values of the user 102 based on theresponses to the choice options. In other words, the expected utilityfunction effectively represents what values the user 102 is actuallyconsidering in answering the choice options, and to what extent the user102 is weighing or considering each of the values. For instance, in theexample depicted in FIGS. 4-6, the user 102 may assign a weight of “5”to cost savings, a weight of “3” to environmental friendliness, and aweight of “1” to locally sourced/manufactured products (at blocks 302,304 of the method 300, for instance), indicating that when making adecision, the user 102 should highly consider (or be influenced by) costsavings, moderately consider environmental friendliness, and slightlyconsider locally sourced/manufactured products.

As depicted in FIG. 6, the user 102 indicates a preference to purchasethe second vehicle (make X, model Y) over the first vehicle (make A,model B). As depicted in FIG. 6, the second vehicle is more expensivethan the first vehicle, the second vehicle is more fuel-efficient thanthe first vehicle, and the second vehicle is made by a foreignmanufacturer, while the first vehicle is made by a domesticmanufacturer. Based on the indication of a preference to purchase thesecond vehicle based on the selections to the one or more choiceoptions, the system 100 may determine that the user 102, is in fact,moderately considering cost savings, moderately consideringenvironmental friendliness, and slightly considering locallysourced/manufactured products. The system 100 may then generate theexpected utility function as a combination of these values and weightsindicated by the selections to the choice options provided by the user102.

Referring again to FIGS. 1-6, at block 316 of the method 300, the system100 compares the expected utility function generated at block 314 to therisk-neutral utility function generated at block 306. As discussed withreference to FIG. 2, the comparison may generally be determining adistance between a curve generated from the risk-neutral utilityfunction and a curve generated from the expected utility function inspace.

Referring still to FIGS. 1-6, at block 318 of the method 300, the system100 assesses the risk of a potential decision based on the comparisonbetween the expected utility function and the risk-neutral utilityfunction. As discussed with reference to FIG. 2, based on the magnitudeof the distance between the expected utility function curve and therisk-neutral utility function curve, the system 100 may determinewhether the potential decision to the prompt requiring a decisionindicated by the answers to the choice options is a risk or not. Thesystem 100 may present the user 102 with a simple determination onwhether the potential decision is a risk or not. For instance, thesystem 100 may present a message to the user 102, “This choice is arisk.” The system 100 may also present a score of the potentialdecision. The score may simply be the distance measured between theexpected utility function and the risk-neutral utility function. Thedistance may also be normalized on a scale to present the risk score orvalue that relates to the measured distance.

Depending on the direction of error, or the orientation of the distance,between the risk-neutral utility function and the expected utilityfunction, for instance, the system 100 may also determine if a potentialdecision to the prompt requiring a decision is risk-seeking orrisk-averse. In the example depicted in FIG. 6, based on the user 102weighing cost savings less than anticipated based on the determinedweighted values of the user 102, the system 100 may determine that thedecision to purchase the second vehicle (make X, model Y) over the firstvehicle (make A, model B) is a risk-seeking decision with a specificrisk value.

Referring still to FIGS. 1-6, at block 320 of the method 300, the system100 may determine a decision to the prompt requiring a decision thatminimizes risk. For instance, the system 100 may generate an expectedutility function for all potential decisions to the prompt requiring adecision, determine the expected utility function that most closelyaligns with the risk-neutral utility function, and present the decisionassociated with the close-aligning expected utility function as thedecision that minimizes risk for the user 102.

In some cases, the system 100 may only analyze the potential decisionsbeing directly considered by the user 102. For instance, in the exampledepicted in FIGS. 4-6, the system 100 may only consider expected utilityfunctions related to decisions to purchase the first vehicle (make A,model B) or the second vehicle (make X, model Y). The system 100 mayfurther determine that the expected utility function associated with thedecision to purchase the first vehicle more closely aligns with therisk-neutral utility function than the expected utility functionassociated with the decision to purchase the second vehicle.Accordingly, the decision to purchase the first vehicle is less riskythan the decision to purchase the second decision.

In other cases, the system 100 may generate expected utility functionsfor potential decisions to the prompt requiring a decision that were notdirectly presented to the user. For instance, based on data accessedfrom the external device 130 and/or data storage component 206, thesystem 100 may generate an expected utility function for a third vehicle(make M, model N, for instance) that the user 102 is not currentlyconsidering based on the presented choice options. The system 100 maydetermine that the expected utility function associated with the thirdvehicle, in fact, most closely aligns with the risk-neutral utilityfunction, indicating that the user 102 may wish to consider the thirdvehicle for purchase even she is not currently considering or aware ofthe third vehicle.

Still referring to FIGS. 1-6, at block 322 of the method 300, the system100 may display the risk of the potential decision and/or the decisionthat minimizes risk on the user device 103. In the example depicted inFIG. 6, the system 100 displays a risk value associated with thedecision to purchase the second vehicle (make X, model Y) over the firstvehicle (make A, model B) and the determination that the potentialdecision is risk-seeking. The system 100 may also present an indicationto the user 102 that a decision to purchase the first vehicle wouldminimize risk. As explained above, the system 100 may also apprise theuser 102 that a third vehicle not yet considered by the user 102 mostclosely aligns with the risk-neutral utility function and would minimizerisk for the user 102.

Still referring to FIGS. 1-6, at block 324 of the method 300, the system100 re-determines the weight of each weighted user value and therisk-neutral utility function based on the user selections to the one ormore choice options. The system 100 may re-determine the weights of theuser values and the risk-neutral utility function based on theimmediately completed risk assessment exercise or provided decision tothe prompt requiring a decision. In other cases, the system 100 mayre-determine the weights of the user values and the risk-neutral utilityfunction based on the five most recent risk assessment exercises orprovided decisions to prompts requiring a decision, for example.

The system 100 may improve the accuracy of the weighted user values andthe risk-neutral utility function based on the user-provided selectionsand answers to the choice options and prompts requiring a decision. Forinstance, when generating a profile, the user 102 may indicate that costsavings is a very important value to her, assigning a weight of “5” tothe value. However, during risk assessment exercises, the user 102 maycontinuously show a preference for expensive decisions, or decisionsthat, at least, do not strongly value cost savings. Based on theseresponses, the system 100 may determine that cost savings is not, infact, a highly important value to the user 102.

Over time, therefore, the system 100 may adjust the originally assignedweight of the cost savings value to more closely align with thedecision-making tendencies regularly displayed by the user 102. That is,the system 100 may adjust the weight of the cost savings value to a “4”if the user 102 regularly shows disregard for cost savings when makingdecisions. With the re-determined weights of the values of the user 102,the system 100 may also re-determine the risk-neutral utility functionbased on the weighted user values. Over time, the risk-neutral utilityfunction may be tailored to accurately capture the true values of theuser 102 and to what extent the user 102 actually weighs each value.

It should be appreciated that the method 300 discussed above is notlimited to the order of steps presented in FIG. 3. For instance, in someembodiments, the risk-neutral utility function generated at block 306may be generated any time prior to the comparison of the expectedutility function to the risk-neutral utility function at block 316. Itshould also be appreciated that steps presented in FIG. 3 need to not bediscrete in all embodiments. That is, the system 100 may determine oneor more user values and determine a weight of each user value togenerate one or more weighted values substantially simultaneously, suchthat blocks 302 and 304 may be considered a single step in the method300. Moreover, it should be appreciated that one or more steps of themethod 300 depicted in FIG. 3 may be omitted from the method 300. Forinstance, in some embodiments, the system 100 may not determine adecision that minimizes risk at block 320. Additionally, one or moresteps not presented in the method 300 depicted in FIG. 3 may becompleted by the system 100.

Based on the foregoing, it should now be understood that embodimentsshown and described herein relate to systems and methods for determininga risk of a potential decision. The system collects data on a user todetermine one or more values of the user that influence the user'sdecision-making process. The system also collects data to determine aweight of each of the values, indicating an extent to which the userconsiders each value when making a decision. Based on the weightedvalues, the system generates a risk-neutral utility function, indicatinga perfectly neutral decision-making process that appropriately considerseach value of the user.

The system compares a potential decision to the risk-neutral utilityfunction to determine the risk of the potential decision. Moreparticularly, the system determines a prompt, or ultimate question theuser desires guidance on, requiring a decision. The system then presentsthe user with one or more choice options pertaining to the promptrequiring a decision and related to the user values. That is, the choiceoptions provide information on the prompt requiring a decision thatrelate to the values of the user, and the answers or selections to thechoice options provide clarity to the user and the system as to whatpotential decision the user is likely to make.

The system may generate an expected utility function based on theanswers to the choice options, indicating what values were, in fact,considered by the user, and to what extent, when answering the choiceoptions. By comparing the risk-neutral utility function and the expectedutility function, the system may then determine the risk of thepotential decision, or the extent that the potential decision deviatesfrom a risk-neutral, anticipated decision or decision-making process ofthe user.

As used herein, the term “about” means that amounts, sizes,formulations, parameters, and other quantities and characteristics arenot and need not be exact, but may be approximate and/or larger orsmaller, as desired, reflecting tolerances, conversion factors, roundingoff, measurement error and the like, and other factors known to those ofskill in the art. When the term “about” is used in describing a value oran end-point of a range, the specific value or end-point referred to isincluded. Whether or not a numerical value or end-point of a range inthe specification recites “about,” two embodiments are described: onemodified by “about,” and one not modified by “about.” It will be furtherunderstood that the endpoints of each of the ranges are significant bothin relation to the other endpoint, and independently of the otherendpoint.

Directional terms as used herein—for example up, down, right, left,front, back, top, bottom—are made only with reference to the figures asdrawn and are not intended to imply ab solute orientation.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order, nor that with any apparatus specificorientations be required. Accordingly, where a method claim does notactually recite an order to be followed by its steps, or that anyapparatus claim does not actually recite an order or orientation toindividual components, or it is not otherwise specifically stated in theclaims or description that the steps are to be limited to a specificorder, or that a specific order or orientation to components of anapparatus is not recited, it is in no way intended that an order ororientation be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, including: matters of logic withrespect to arrangement of steps, operational flow, order of components,or orientation of components; plain meaning derived from grammaticalorganization or punctuation, and; the number or type of embodimentsdescribed in the specification.

As used herein, the singular forms “a,” “an” and “the” include pluralreferents unless the context clearly dictates otherwise. Thus, forexample, reference to “a” component includes aspects having two or moresuch components, unless the context clearly indicates otherwise.

For the purposes of describing and defining the present subject matter,it is noted that reference herein to a variable being a “function” of aparameter or another variable is not intended to denote that thevariable is exclusively a function of the listed parameter or variable.Rather, reference herein to a variable that is a “function” of a listedparameter is intended to be open ended such that the variable may be afunction of a single parameter or a plurality of parameters.

It is noted that recitations herein of a component of the presentdisclosure being “configured” or “programmed” in a particular way, toembody a particular property, or function in a particular manner, arestructural recitations, as opposed to recitations of intended use. Morespecifically, the references herein to the manner in which a componentis “programmed” or “configured” denotes an existing physical conditionof the component and, as such, is to be taken as a definite recitationof the structural characteristics of the component.

It is noted that terms like “preferable,” “typical,” and “suitable” whenutilized herein, are not utilized to limit the scope of the claimedsubject matter or to imply that certain features are critical,essential, or even important to the structure or function of the claimedsubject matter. Rather, these terms are merely intended to identifyparticular aspects of an embodiment of the present disclosure or toemphasize alternative or additional features that may or may not beutilized in a particular embodiment of the present disclosure.

For the purposes of describing and defining the present subject matterit is noted that the terms “substantially” and “approximately” areutilized herein to represent the inherent degree of uncertainty that maybe attributed to any quantitative comparison, value, measurement, orother representation. The terms “substantially” and “approximately” arealso utilized herein to represent the degree by which a quantitativerepresentation may vary from a stated reference without resulting in achange in the basic function of the subject matter at issue.

Having described the subject matter of the present disclosure in detailand by reference to specific embodiments thereof, it is noted that thevarious details disclosed herein should not be taken to imply that thesedetails relate to elements that are essential components of the variousembodiments described herein, even in cases where a particular elementis illustrated in each of the drawings that accompany the presentdescription. Further, it will be apparent that modifications andvariations are possible without departing from the scope of the presentdisclosure, including, but not limited to, embodiments defined in theappended claims. More specifically, although some aspects of the presentdisclosure are identified herein as preferred or particularlyadvantageous, it is contemplated that the present disclosure is notnecessarily limited to these aspects.

What is claimed is:
 1. A method implemented by a processor of a device,the method comprising: determining one or more user values; determininga prompt requiring a decision; receiving a user selection to one or morechoice options related to the one or more user values and pertaining tothe prompt requiring a decision; comparing the user selection to arisk-neutral utility function; and assessing a risk of a potentialdecision based on the comparison.
 2. The method of claim 1, furthercomprising: generating an expected utility function based on the userselection; and comparing the expected utility function to therisk-neutral utility function.
 3. The method of claim 2, furthercomprising: assessing the risk of the potential decision based on thecomparison between the expected utility function and the risk-neutralutility function.
 4. The method of claim 1, further comprising:generating the risk-neutral utility function based on the one or moreuser values.
 5. The method of claim 1, wherein the potential decision isa decision to the prompt requiring a decision.
 6. The method of claim 1,further comprising: determining a weight of each user value to generateone or more weighted user values; and generating the risk-neutralutility function as a linear combination of each weighted user value. 7.The method of claim 6, further comprising: determining an updated weightof each user value based on the user selection to the one or more choiceoptions.
 8. The method of claim 1, wherein assessing the risk of thepotential decision based on the comparison further comprises:determining whether the potential decision is risk-seeking orrisk-averse.
 9. The method of claim 1, further comprising: outputting,on a display of the device, the risk of the potential decision.
 10. Asystem comprising: a device including a processor configured to:determine one or more user values; determine a prompt requiring adecision; receive a user selection to one or more choice options relatedto the one or more user values and pertaining to the prompt requiring adecision; compare the user selection to a risk-neutral utility function;and assess a risk of a potential decision based on the comparison. 11.The system of claim 10, wherein the processor is further configured to:generate an expected utility function based on the user selection;compare the expected utility function to the risk-neutral utilityfunction; and assess the risk of the potential decision based on thecomparison between the expected utility function and the risk-neutralutility function.
 12. The system of claim 10, wherein the processor isfurther configured to: generate the risk-neutral utility function basedon the one or more user values.
 13. The system of claim 10, wherein theprocessor is further configured to: determine a weight of each uservalue to generate one or more weighted user values; and generate therisk-neutral utility function as a linear combination of each weighteduser value.
 14. The system of claim 13, wherein the processor is furtherconfigured to: determine an updated weight of each user value based onthe user selection to the one or more choice options.
 15. The system ofclaim 10, wherein the processor is further configured to: output, on adisplay of the device, the risk of the potential decision.
 16. Aprocessor of a computing device, the processor configured to: determineone or more user values; determine a prompt requiring a decision;receive a user selection to one or more choice options related to theone or more user values and pertaining to the prompt requiring adecision; compare the user selection to a risk-neutral utility function;and assess a risk of a potential decision based on the comparison. 17.The processor of claim 16, further configured to: generate an expectedutility function based on the user selection; compare the expectedutility function to the risk-neutral utility function; and assess therisk of the potential decision based on the comparison between theexpected utility function and the risk-neutral utility function.
 18. Theprocessor of claim 16, further configured to: generate the risk-neutralutility function based on the one or more user values.
 19. The processorof claim 16, further configured to: determine a weight of each uservalue to generate one or more weighted user values; generate therisk-neutral utility function as a linear combination of each weighteduser value; and determine an updated weight of each user value based onthe user selection to the one or more choice options.
 20. The processorof claim 16, further configured to: output, on a display associated withthe computing device, the risk of the potential decision.